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FATMA ÇINAR, MBA, Capital Markets Board of Turkeye-mail: [email protected] @fatma_cinar_ftm, @TRUserGroup
KUTLU MERİH, PhD e-mail: [email protected] @cortexien https://www.riskonomi.com
35th National Operations Research and Industrial Engineering Congress (ORIE 2015) 09-11,September, 2015,Middle East
Technical University, Ankara, Turkey.
SECTORAL LOANS DEFAULT CHART OF SECTORAL LOANS DEFAULT CHART OF TURKEYTURKEY
Wednesday, September 02, 2015
A baloon emerging on the importance of Big DataBig Data is not large data, Big Data is complex data.Strategical datasets may not big but quite complex
What is big data?
How Big Data Humour is big!
Monday, May 1, 2023
Purpose of the study
• We aim to decipher this complexity of datasets With advanced graphical packages of R software
• Our Technique is Graphical Datamining
• To Analyse various credit and financial situation of EnergyLoans and EnergyLoanDefaults of some of the cities and regions of Turkey.
• Our Dataset will be FINTURK of BRSA
Monday, May 1, 2023
Agenda
• Case study: BRSA NUTS and Sectoral Loans Default Chart of Turkey
Data: BRSA* and NUTS of Turkey (Nomenclature of Territorial Units for Statistics, NUTS
Dataset: NUTS Region Investment Promotion and 3 account period Graphical Datamining Analysis of FINTURK of BRSAPeriod: 2012-2014 AccountsDataset are factorized according to city, year, sector and region factors. Graphical Datamining applied on this factorized data.
*BRSA: Banking Regulations and Supervisison Agency
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Action
• Real Time Interactive Data Management for
• Effect and Response Analysis
Technique: • Graphical Datamining using #ggplot2
Graphical Package of #R Software
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Sectoral Loans Dataset Graphics Data-Mining Analysis
names(dataset)• names(dataset)• [1] "NYEAR" "SYEAR" "QUARTERS" • [4] "CITY" "CITYCODE" "NREGION" • [7] "REGION" "NUTS3CODE" "NUTS2CODE" • [10] "NUTS1CODE" "TRNUTS1REGION" "NUTS1REGION" • [13] "TRGROUP" "SECTORAL" "CASHLOANS" • [16] "NONCASHLOANS" "TOTALCASHLOANS" "AUTO" • [19] "MORTGAGE" "OVERDRAFTACCOUNT" "CREDITCARDS" • [22] "FOOD" "BUILDING" "MINERALS " • [25] "FINANCIAL" "TEXTILE" "WHOSESALE " • [28] "TOURISM" "AGRICULTURE" "ENERGY" • [31] "MARITIME" "OTHERCONSUMER" "DEFRECEIVABLE" • [34] "DEFCREDITCARDS" "DEFAUTO" "DEFMORTGAGE" • [37] "DEFOTHERCONSUMER" "DEFFOOD" "DEFBUILDING" • [40] "DEFMINERALS" "DEFFINANCIAL" "DEFTEXTILE" • [43] "DEFWHOLESALE " "DEFTOURISM" "DEFAGRICULTURE" • [46] "DEFENERGY" "DEFMARITIME" "NONCASHFOOD" • [49] "NONCAHBUILDING" "NONCASHMINERALS" "NONFINANCIAL" • [52] "NONCASHTEXTILE" "NONCASHWHOLESALE " "NONCASHTOURISM" • [55] "NONCASHAGRICULTURE" "NONCASHENERGY" "NONCASHMARITIME"
Monday, May 1, 2023
NUTS of Turkey (Nomenclature of Territorial Units for Statistics, NUTS)
NUTS-1:12 Region of Turkey
• MEDITERRANEAN• SOUTHEAST ANATOLIA• EAGEAN REGION• NORTHEAST ANATOLIA• MIDDLE ANATOLIA• WEST BLACK SEA• WEST ANATOLIA• EAST BLACK SEA• WEST MARMARA• MIDDLE EAST ANATOLIA• ISTANBUL• EAST MARMARA
•NUTS-1: 12 Regions•NUTS-2: 26 Subregions•NUTS-3: 81 Provinces
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(Nomenclature of Territorial Units for Statistics, NUTS)
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Monday, May 1, 2023
İstanbul Region
West Marmara
Region
Aegean Region
East Marmara
West Anatolia Region
Mediterranean Region
Anatolia Region
West Black Sea Region
East Black Sea Region
Northeast Anatolia Region
East Anatolia Region
Southeast
Anatolia
İstanbul (Subregion)
Tekirdağ (Subregion)
İzmir (Subregion)
Bursa (Subregion)
Ankara (Subregion)
Antalya (Subregion)
Kırıkkale (Subregion)
Zonguldak (Subregion)
Trabzon (Subregion)
Erzurum (Subregion)
Malatya (Subregion)
Gaziantep
(Subregion)
Edirne Aydın (Subregion) Eskişehir Konya
(Subregion) Isparta Aksaray Karabük Ordu Erzincan Elazığ Adıyaman
Kırlareli Denizli Bilecik Karaman Burdur Niğde Bartın Giresun Bayburt Bingöl Kilis
Balıkesir (Subregion) Muğla Kocaeli
(Subregion) Adana (Subregion) Nevşehir Kastamonu
(Subregion) Rize Ağrı (Subregion) Dersim
Şanlıurfa
(Subregion)
Çanakkale Manisa (Subregion) Sakarya Mersin Kırşehir Çankırı Artvin Kars Van
(Subregion)Diyarba
kır
A.Karahisar Düzce Hatay (Subregion)
Kayseri (Subregion) Sinop Gümüşhane Iğdır Muş
Mardin (Subreg
ion)
Kütahya Bolu Kahramanmaraş Sivas Samsun (Subregion) Ardahan Bitlis Batman
Uşak Yalova Osmaniye Yozgat Tokat Hakkari Şırnak
Çorum Siirt
Amasya
1 Province 5 Province 8 Province 8 Province 3 Province 8 Province 8 Province 10 Province 6 Province 7 Province 8 Province9
Province
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We downloaded FINTURK dataset from the site of BRSA and anotated it by NUTS factors.
Our software read this data from an excel file with the name of “dataset”
From now on “dataset” means our NUTS Credit Loans FINTURK data
SOURCE OF THE DATA
What is ggplot2 and Grammar of Graphics ?
Grammer of graphics represents and abstraction of graphics ideas/objects
Think ‘verb’, ‘noun’, ‘adjective’ for graphics Allows for a ‘theory’ of graphics on which to build
new graphics and graphics ogjects ‘Shorten the distence from mind to page’ Created by Hadley WICKHAM of Rice University @hadleywickham
Monday, May 1, 2023
ggplot2 Graphics Package
• How to create basic plots (xyplot, scatterplots, histograms, baloon, facet, density and violin) using ggplot() function of ggplot2 package
• Setting vs. mapping• How to add extra variables with aesthetics (like
color, shape, and size) or faceting
• http://docs.ggplot2.org/current/
Monday, May 1, 2023
Grammer of Graphics ?
‘In brief, the grammer tells us that a statistical graphic is a mapping from data to aesthetic attributes (color, shape, size) of geometric object (point, lines, bars).The plot may also contain stastistical transformations of data and drawn on a specific coordinate system’
Hadley Wickham
Monday, May 1, 2023
Styles of Graphs
• We apply four types off ggplot2 graphical styles with ggplot2 geoms
1. Scatterplot with geom_point()2. Densityplot with geom_density()3. Violinplot with geom_violin()4. Smooth with geom_smooth()and facetplot with facet_grid()
Monday, May 1, 2023
Description of Baloon
Graphs
Baloon graphs of ggplot2 package can show us
3-dimensional relations distributed according 1-3
factors in scatterplot form.
With this type 2-dimensional numerical relations
can be represented under effect of 3rd numerical
value.
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library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY), size=DEFRECEIVABLE,color=SECTORAL)gs<-geom_point()ps<-ds+ae+gsprint(ps)
ggplot2 commands for scatterplot graphs
NUTS Regions Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by Sectoral and Year Factors Grid
Graphics
Eagean Regions Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by City, Sectoral and Year Factors Grid
Graphics
Monday, May 1, 2023
Eagean Regions Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by Sectoral and Year Factors Grid
Graphics
İzmir Province Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by City, Year and Sectoral Factors Grid
Graphics
İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Quarters, Years and Sectoral
Factors Grid Graphics
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• Density Graphs are the continuous version of Histograms
• They plot a single numerical variable against their frequency.
• We can detect single or multiple peaks of density graphs and pinpoint the effective factors.
• On the other hand soperposing density graphs acording the factors with different colors provide us with information of the effect of the factors
• Logarithmic scale leads a more stable density formations for financial data.
Description of Density
Graphs
Monday, May 1, 2023
library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(DEFENERGY), color=SECTORAL)gd<-geom_density(alpha=0.5)pd<-ds+ae+gdprint(pd)
ggplot2 commands for density graphs
NUTS Regions Log10 Default Energy, Explained by Years Factors Density Graphic by Log10DefEnergy
Eagean Regions and Province Log10 Default Energy, Explained by City Factors
Density Graphic by Log10 DefEnergy
Monday, May 1, 2023
Eagean Regions and Provinces Log10 Default Energy, Explained by Years Factors
Density Graphic of DefEnergy
İzmir Province Log10 Default Energy, Explained by Quarters Factors
Density Graphic by DefEnergy
Monday, May 1, 2023
Description of Violin Graphs
• Violin Graphs can be seen as two-dimensional density graphs
• Violin Graphs are very important for Risk Analysis of financial Data
• Through the mean of X-axis Y-density graph occurs with mirror copy
• Usually Violin Graphs comes with Mushroom, Pottery and Bottle formations
• Mushroom formation represents a risk concentration on hig order values of financial data
• Pottery means risk on the medium order
• and the bottle menas risk on the lower orders
Monday, May 1, 2023
library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY), color=SECTORAL)gv<-geom_violin(alpha=0.5)pv<-ds+ae+gvprint(pv)
ggplot2 commands for violin graphs
NUTS Regions Log10 Default Energy, Explained by Years Factors
Violin Graphic
Monday, May 1, 2023NUTS Regions Log10 Energy, Explained by Sectoral Factors
Violin Graphic
Eagean Regions and Province Log10 Default Energy,Explained by City Factors
Violin Graphic
Monday, May 1, 2023
Eagean Regions and Province Log10 Energy, Explained by Years Factors Violin Graphic
Monday, May 1, 2023
İzmir Province Log10 Default Energy, Explained by Sectoral Factors
Violin Graphic
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2 by 2 COMBINED DENSITY AND VIOLIN
GRAPHICS
Monday, May 1, 2023
NUTS Regions Log10 Energy Vs Log10 Default Energy Explained by Years Factors Density/Violin Graphics
Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City Factors
Density/Violin Graphics
Monday, May 1, 2023
Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Explained by Years Factors
Density/Violin Graphics
Monday, May 1, 2023İzmir Province Log10 Energy Vs Log10 Default Energy,Explained
by Quarters Factors Density/Violin Graphics
Monday, May 1, 2023
Description of
PowerLaw Graphs
Power law distributions are usually used to model data whose frequency of an event varies as a power of some attribute of that event.
A numeric constant giving the power-law exponent and describes a scale free rate of change
Our POWERLAW ANALYSIS DataMining technique basicly a straigt line smoothing of double logatithmic data
This technique especially powerful on describing Risk Profiles of financial data
Our procedure for analyzing the data will follow the procedure in the paper: POWERLAW
Monday, May 1, 2023
library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY), color=SECTORAL)gp<-geom_point()ss<-stat_smooth(method = "lm", formula = y ~ x, size = 2)pl<-ds+ae+gp+ssprint(pl)
ggplot2 commands for Power Law Smooth graphs
Nuts Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Sectoral Factors
PowerLaw Graphics
Monday, May 1, 2023
Nuts Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Nuts1Regions Factors
PowerLaw Graphics
Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City Factors
PowerLaw Graphics
Monday, May 1, 2023
İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Years Factors
PowerLaw Graphics
Monday, May 1, 2023
İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City Factor
PowerLaw Graphics
Monday, May 1, 2023
İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Quarters Factors
PowerLaw Graphics
Monday, May 1, 2023
İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Sectoral Factors
PowerLaw Graphics
Monday, May 1, 2023
• Facet graphs of ggplot2 package can show us 3-dimensional graphs distributed according 3 factors in matrix form.
• In which we can see the anomalies occurs on which year and which region and which period.
• Here we investigate Energy versus default Energy balooned by Total Default Receivables according to region, year and period factors.
• Colors period, balloons Total Cash loans.
Description of Facet Graphs
library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY),
color=SECTORAL)gp<-geom_point()fs<-facet_grid(“factor1” ~ “facror2”)pf<-ds+ae+gp+fsprint(pf)
Monday, May 1, 2023
ggplot2 commands for facetting
Nuts Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Nuts1Regions Factors
Facet Graphic
Monday, May 1, 2023
İzmir Province Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Year and Sectoral Factors
Facet Graphic
Monday, May 1, 2023
Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City,Year and Sectoral Factors
Facet Graphic
With this study we investigate NUTS 12 Regions credit loans performations by Graphical Datamining Analysis technique with a suitable software developed by us.
Dataset are factorized according to cities and years, sectorals and financial periods factors.
Periods: 2012-2014 accounts. Graphical Datamining applied on this factorized
data and financial anomalies dedected acording to time and space factors.
We observes apparently obvious differences of risk profiles affected by these factors
It is quite clear that pictures tells more stories than numbers
Monday, May 1, 2023
We would like to express our deep gratitude to;
Dr. C. Coşkun KÜÇÜKÖZMENfor their valuable contibutions,
Fatma CINARKutlu MERIH
Contact
@TRUserGroup@CORTEXIEN@Riskonometri@Riskonomi@datanalitik@Riskanalitigi@RiskLabTurkey@fatma_cinar_ftmtr.linkedin.com/in/fatmacinartr.linkedin.com/pub/kutlu-merihtr.linkedin.com/in/coskunkucukozmen
[email protected]@ieu.edu.trhttp://www.ieu.edu.tr/tr [email protected]://[email protected]
http://www.spk.gov.tr/
http://www.riskonomi.com
Monday, May 1, 2023
Küçüközmen, C. C. and Çınar F., (2014). “Modelling of Corporate Performance In Multi-Dimensional Complex Structured Organizations “CBBC” Management”, Submitted to the “2nd International Symposium on Chaos, Complexity and Leadership (ICCLS), December 17-19 at Middle East Technical University (METU), Ankara, Turkey.Küçüközmen, C. C. ve Çınar F., (2014). “Finansal Karar Süreçlerinde Grafik-Datamining Analizi”, TROUGBI/DW SIG, Nisan 2014 İstanbul, http://www.troug.org/?p=684 Küçüközmen, C. C. ve Çınar F., (2014). “Görsel Veri Analizinde Devrim” Söyleşi, Ekonomik Çözüm, Temmuz 2014, http://ekonomik-cozum.com.tr/gorsel-veri-analizinde-devrim-mi.html.Küçüközmen, C. C. ve Merih K., (2014). “Görsel Teknikler Çağı" Söyleşi, Ekonomik Çözüm, Temmuz 2014, http://ekonomik-cozum.com.tr/gorsel-teknikler-cagi.htmlKüçüközmen, C. C. and Çınar F., (2014). “Banking Sector Analysis of Izmir Province: A Graphical Data Mining Approach”, Submitted to the 34th National Conference for Operations Research and Industrial Engineering (YAEM 2014), Görükle Campus of Uludağ University in Bursa, Turkey on 25-27 June 2014. Merih, K. ve Çınar, F., (2013). “Modelling of Corporate Performance In Multi-Dimensional Complex Structured Organizations: “Cbbc” Approach”, Submitted to the EconAnadolu 2013: Anadolu International Conference in Economics III June 19-21, 2013, Eskişehir. http://www.econanadolu.org/en/index.php/articles2013/3683Küçüközmen, C. C. and Çınar F., (2014). “New Sectoral Incentive System and Credit Defaults: Graphic-Data Mining Analysis”, Submitted to the ICEF 2014 Conference, Yıldız Technical University in İstanbul, Turkey on 08-09 Sep. 2014.Pedroni M., and Bertrand Meyer (2009). “Object-oriented modeling of Object-Oriented Concepts”, ‘A Case Study in Structuring an Educational Domain’, Chair of Software Engineering, ETH Zurich, Switzerland. fmichela.pedroni|[email protected]üçüközmen, C. C. and Çınar F., (2015). “Visual Anaysis of Electricity Demand Energy Dashboard Graphics” Submitted to the 5th Multinational Energy and Value Conference May 7-9, 2015 Kadir Has University in İstanbul, Turkey
